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Various computing subdisciplines, such as computer science and software engineering, each have their own curricular guidelines. They can be very difficult to understand and compare for people such as prospective students, industry personnel, and even faculty members. This is compounded by a lack of information surrounding undergraduate computing curricular topics via visual methods. This paper describes two experimental activities where the objective is to explore the possibility of obtaining quantitative data sets necessary for visualization, one based on competencies and the other based on knowledge areas. Both activities were based on surveys. The results from the first activity showed that a consensus interpretation could be obtained for the knowledge, skills, and dispositions implied by the competency descriptions, although not as strongly for dispositions. The second activity resulted in a table of knowledge areas with minimum and maximum weights for six computing subdisciplines. Finally, this paper also shows two examples of how users can explore the various curricular guidelines through visualization.

In recent years, a lot of attention has been given to patterns in the Internet of Things (IoT). In essence, these patterns provide well-known ways to solve problems commonly encountered in the IoT paradigm. However, despite this recognition, conceptualising patterns in the IoT has proven to be difficult. This is mainly because IoT patterns come in many shapes, forms and sizes, and can be abstracted from many levels of the system architecture. In fact, many patterns in the IoT are system-independent and can be abstracted from other fundamental aspects that make up the IoT. This seemingly broad variations of patterns has led to some confusion regarding what constitute a pattern in the IoT. This has raised many unanswered questions in the IoT pattern community, such as (i) if patterns are not only about objects, and if they reach far beyond software architecture, then what is an IoT pattern?, and (ii) what are the solution categories in which the underlying IoT patterns can be abstracted? With these questions in mind, we set out to perform a deeper analysis of the meaning of the word pattern in the context of the IoT. We discovered that the IoT patterns give us a standardised vocabulary to talk about structures larger than objects, modules and procedures. That is, patterns are, in fact, a literature that is ingrained in problem solving and design thinking in the holistic IoT domain.

The Internet of Things (IoT) consists of several patterns that address a variety of problems encountered in the design and construction of reference architectures. Essentially, these patterns represent a set of conventional solutions and technologies that are used in the IoT paradigm to create ambient intelligence. However, in order for the IoT practitioners to use these patterns effectively, a catalogue that groups and classifies them in a systematic manner is required. Such a catalogue must facilitate easy and quick retrieval of the pattern, and clearly describe the intended purpose of each pattern. At the moment, no such catalogue exists, and the reviewed catalogues in the literature are either cumbersome or address these issues inadequately. This makes it difficult for users and the IoT practitioners to easily choose the relevant pattern for the given problem. Due to the large number of the IoT patterns, it is difficult to organise them in a manner that facilitate systematic classification. In this study, we propose a methodical, fivestep approach by which we can arrange these patterns for easy identification. The nature of this paper is theoretical, thus we focus on exploring some linguistic theories and algebraic structures that make up the core essence of this multifaceted process.

Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.

The Internet of Things patterns dictionary represents an aspect of a lexicon - it provides a set of definitions for the IoT patterns. This dictionary fulfills a function in language standardization for the IoT paradigm by providing patterns definitions that are based on a number of parameters. Definitions convey the meaning of a concept to the audience. In the field of linguistics, various types of definitions exist - each with a different purpose and focus. However, since a definition expresses the meaning of a word using other words, forming clear descriptive definitions can be a challenging venture. In the literature, definitions for patterns are often formed based on the author's knowledge and intuition without following any formal blueprint. In this paper, a systematic method for constructing lexical descriptive definitions for the IoT patterns is proposed. The aim is to evolve the theoretical framework presented here into a formal IoT patterns dictionary. Since it is common knowledge that each IoT pattern consists of a set of attributes, one can, in practice, derive a lexical definition for each pattern based on these attributes. In linguistics, the meaning of a lexical term is often derived from a combination of different components describing that particular object. Similarly, a definition of each IoT pattern is composed of at least one or more general and/or specific attributes that essentially, when juxstaposed, make up a unique definition of each pattern. Of course, our approach to definition formation is linguistics-based, and not rooted in software engineering. Thus, most of the content in this paper is dedicated to the discussion of several mechanisms for defining languages and showing that the attributes of a given pattern can be concatenated using an algebraic linguistics modus operandi to construct formal definitions.

Automatic Indexing of documents using paragraph vectors is a popular unsupervised method for learning distributed representations of texts. This method learns embedding of words with document vectors for document classification. In addition, this method can be leveraged for sentiment analysis. However, while the results presented in the original Doc2Vec study were promising, the overall proof of concept was rather narrow. In this study, we extend the Doc2Vec method, and enhance it to classify locality sensitive documents, i.e. domain-based documents which are largely similar with marginal differences. In particular, we use the enhanced Doc2Vec technique to classify similar documents describing the Internet of Things (IoT) patterns. We observed that our enhanced locality sensitive Doc2Vec technique performs significantly well to improve embedding quality. The model performance is in par with state-of-the-art results and can be qualified as a benchmark for similar vector space models.

Proper knowledge representation and conceptual analysis often require clear ontological support. This is particularly true for large domains in which explicit formal specifications of the terms is required for encoding knowledge, thus making it understandable to the end users. In the Internet of Things (IoT), there is a substantial development of solutions for a wide range of devices and IoT platforms. These solutions are based on existing known patterns which require interoperability for seamless connection and cross-platform resource sharing. However, to the best of our knowledge, pattern relationships for facilitating interoperability in the IoT are undocumented. The activity of developing a domain-specific ontology for the interoperability of the IoT patterns is therefore one of the fundamental steps in the organization of the IoT paradigm. It is clear, however, that this activity as well as other engineering activities, particularly with regard to the development of ontologies is often made complex by the use of confusing semantic terminologies and dense ontological structures. This often lead to ontological explicative horrors in which the essence of the ontology is hidden instead of highlighted. In this paper, we thus prefer to develop a lightweight ontology that clearly display relational dimensions between the IoT patterns. We use empirical analysis to identify core objects underneath the interoperability of the IoT technologies and patterns at a large-scale.

The Internet of Things (IoT) is a paradigm with multitudes of design patterns. However, in order to use these patterns quickly and effectively, one must be able to make a differentiation between the existing patterns. At the moment, there is no known catalogue for the IoT patterns in which each pattern is described at a fine-grained level, i.e. in terms of its attributes. The need to discuss these patterns in terms of their attributes is important as it enables ease of understanding and allows us to group related patterns together for speedy retrieval. In this paper, we present an attributes extraction system which generates a list of attributes for a given IoT pattern. The attributes extraction system is based on identification and extraction of important sentences which describe the core properties of the given IoT pattern. The system uses multiple linguistics features to identify the most important sentences in a document with regard to describing the core essence of a given pattern. The system calculates an independent score for each sentence per feature. Through aggregation, the independent scores for each feature can then be combined to give a weighted mean score for each sentence. The evaluation results show that the attributes selected by the system are consistent with human ranking in the bulk of the examined documents.

Most popular industry verticals such as smart health, smart mining, smart agriculture, etc. fail to map clearly the solution patterns used in the Internet of Things (IoT). This causes a disconnection in our understanding of the implementation approaches and solutions used in the IoT paradigm. Nonetheless, there is another way of subdividing this space – decoding the building blocks of the IoT architectures, with a focus on common components and patterns for interoperability. The IoT presents us with several design challenges at all architectural levels. This ranges from the overall architecture at the highest level to device connectivity at the lowest level. In this complex design, it is easy to get lost in the forest of the latest IoT offerings without understanding the underlying solution patterns. In this study, we have mapped several IoT architectural patterns, spanning several industry verticals, by analysing the literature that report on real-world IoT implementations to identify common, recurring solution patterns. In the spirit of the Gang-of-Four 1 and Christopher Alexander’s pattern language, as well as some recent advances in the IoT space, we have identified some systematic methods which are used to organise the IoT patterns.

The study of algorithms which solve the subgraph isomorphism problem is very important because it has many applications where data is modelled as graphs. Despite the subgraph isomorphism problem being NP-hard, research has been dedicated to proposing new algorithms which are designed to improve the shortcomings of the algorithms that have been previously proposed. However, some of the newly invented solutions do not improve on their predecessors and some are redundant because their algorithmic properties are not different from some existing algorithms. The problem arises because minimal effort has been made towards organising subgraph isomorphism algorithms in terms of their relationships and differences such that new solutions utilise knowledge extracted from the organisation of knowledge about these algorithms. The need to organise information has motivated the current study to show how Formal Concept Analysis can be used to build a conceptlattice based taxonomy of algorithms. There are many subgraph isomorphism algorithms and it is not feasible to list all of them. As a result, the implications between the attributes of the algorithms are used to build a representative taxonomy of the domain of subgraph isomorphism algorithms. The paper focuses on how the tools from Formal Concept Analysis can be used to build a taxonomy. This is illustrated using a small sample subset of algorithms.

Curriculum design has changed from the traditional centralised approach where a lecturer was provided with the curriculum and content they had to teach to an approach where lecturers are able to provide their own flavour to the curriculum based on their knowledge and expertise. With the change in society and democratic citizenship, the role of the student in curriculum design is coming to the fore. By enabling students to buy-in to the curriculum they are to be taught provides them with a sense of ownership and motivates them to learn the content presented.In this paper a module presented to fourth year students is used to show how when students are enabled to negotiate their curriculum, the content proposed during the negotiations deviates slightly over the years. From the results, the curricula presented after negotiation may look different in their approach, but all have common themes forming their foundation.

Curricula are not static, especially in Computer Science. Curricula specifications are updated and it is not always clear what the exact updates are nor the impact these updated may have on a curriculum which has been developed to comply with the specification. In this paper, the results of a comparison on the knowledge unit and topic-level between the ACM/IEEE Computer Science curriculum volumes of 2001, 2008 and 2013 is presented.

Humans will again set foot on the moon. The moon is covered by a layer of fine dust, which can pose a respiratory hazard. We investigated the pulmonary toxicity of lunar dust in rats exposed to 0, 2.1, 6.8, 20.8 and 60.6 mg/m(3) of respirable-size lunar dust for 4 weeks (6 h/day, 5 days/week); the aerosols in the nose-only exposure chambers were generated from a jet-mill ground preparation of a lunar soil collected during the Apollo 14 mission. After 4 weeks of exposure to air or lunar dust, groups of five rats were euthanized 1 day, 1 week, 4 weeks or 13 weeks after the last exposure for assessment of pulmonary toxicity. Biomarkers of toxicity assessed in bronchoalveolar fluids showed concentration-dependent changes; biomarkers that showed treatment effects were total cell and neutrophil counts, total protein concentrations and cellular enzymes (lactate dehydrogenase, glutamyl transferase and aspartate transaminase). No statistically significant differences in these biomarkers were detected between rats exposed to air and those exposed to the two low concentrations of lunar dust. Dose-dependent histopathology, including inflammation, septal thickening, fibrosis and granulomas, in the lung was observed at the two higher exposure concentrations. No lesions were detected in rats exposed to ≤6.8 mg/m(3). This 4-week exposure study in rats showed that 6.8 mg/m(3) was the highest no-observable-adverse-effect level (NOAEL). These results will be useful for assessing the health risk to humans of exposure to lunar dust, establishing human exposure limits and guiding the design of dust mitigation systems in lunar landers or habitats.

Many businesses see Big Data and Data Science as a catalyst for innovation. The problem is that many of these businesses are hesitant to embrace these new technologies mainly because of a shortage in skilled manpower. On a global level, higher education institutions are in the process of developing curricula for graduate degree programs relating to Big Data and Data Science. Developing such curriculum has its own challenges. For example: What level of knowledge is required from disciplines such as Computing and Statistics? What underlying foundations in Mathematics are required? This paper presents a framework for the design of an interdisciplinary Big Data and Data Science curriculum on the Master’s level.

Digraphs are used to model real world systems and therefore the need arises to be able to determine the similarity between two relatively large digraphs. This similarity is in terms of both the structure of the digraphs and the knowledge represented by their vertices and edges.Two algorithms are presented which derive their basic functionality from the Levenshtein distance algorithm. The Levenshtein algorithm is used in string comparisons to determine similarity. These two algorithms, not only calculate the edit distance between two digraphs, but preserve the operations used to convert one digraph into the other.The results of the two algorithms are discussed in terms their edit distance cost results and their execution speed. These results are contrasted with one another and with the Levenshtein distance algorithm adapted for representing digraphs. The results indicate that the two proposed algorithms are more accurate in terms of their edit distance calculations than the Levenshtein distance algorithm, but that the Levenshtein algorithm out performs them in terms of execution speed.

A pilot study was conducted to compare gauze with silicone foam that may be left in place for as long as seven days. Adult patients who were receiving treatment via peripherally inserted central catheters were recruited and alternately assigned to either the gauze or silicone foam group. Patient-reported itch and discomfort, nurse-reported ease of removal, and skin status were recorded for four weeks at each weekly dressing change. At a Glance • Peripherally inserted central catheters (PICCs) usually remain in place for the duration of chemotherapy treatment with dressings changed on a weekly basis. • A gauze cushioning barrier may be used to protect the skin against the PICC hub; however, guidelines recommend that gauze is changed every 24–48 hours. • Silicone foam may be superior to gauze as a cushioning barrier and is appropriate for weekly dressing maintenance. Refereed/Peer-reviewed

This article will describe how occupational therapists in Toronto, Canada, use the Safety Assessment of Function and the Environment for Rehabilitation (SAFER) tool and the SAFER tool manual to conduct home safety assessments and interventions. Interview, observation and task performance are used by therapists to complete the 97-item SAFER tool, which includes intervention strategies such as client and caregiver education. Further developments of the SAFER tool will be briefly described. Key words: occupational therapy, community, environmental safety, assessment, treatment, intervention